As a technique to represent the multivalued image (gray-scale image) by a small quantity of data, a pseudo-gradation image (half-toning) is used. For example, in an apparatus to only output an image data of small scale pixel such as binary (1 bit) or four values (two bits), the multivalued image of large scale pixel such as 256 values (8 bits) per one pixel is recorded or displayed by using the pseudo-gradation image. In this method, as for spatially averaging ability of a human's eye, gradation of display is falsely represented by average of pixel density per local area. A dithering in printing technique is one of the pseudo-gradation image. The pseudo-gradation image can represent the multivalued image by the small quantity of data and it is used in image communication between the apparatuses.
As one of the pseudo-gradation image, an error-diffusion method is used. In the error-diffusion method, density of a notice pixel in an original image is quantized to a small scale (binary) coincident to expression ability of the apparatus. A quantized error is added to density of unprocessed pixel neighboring the notice pixel in the original image in order to compensate the density of the original image. This method is suprior to the dithering in respect of detail expression and widely used in the image communication between the apparatuses. However, in the error-diffusion method, granular texture often appears in a converted image and the observer receives noise feeling as granular rough pattern. Furthermore, in an image area which density is constant, texture noise of snake-like uniquely appears in the error-diffusion image.
Recently, gradation expression ability of the image output apparatus grows up and a large gradation compared with original gradation of the pseudo-gradation image can be represented. As a result, a multivalued technique to convert gray-scale number per one pixel of the error-diffusion image to higher gray-scale number in order to reduce the texture noise and the rough feeling. As a representative method of the multivalued technique, multivalued estimation method is used. In the multivalued-estimation method, a window consisted of predetermined number of pixels along main scanning direction and sub scanning direction is formed as a center of the notice pixel in the binary image. An average value of density of all pixels in the window is calculated and this average value is used as multivalued-data of the notice pixel. However, in this method, the multivalued-data is only determined by average density of all pixels in the window and pixel pattern formed by distribution of pixel density in the window is not taken into consideration. Therefore, snake-like texture noise still remains in the multivalued image and this noise is further emphasized. Furthermore, in the image processing apparatus to use the multivalued-estimation method, size of the window is limited by capacity of a line buffer. Therefore, if the capacity of the line buffer is small, the density average of pixels in wide area is difficult to be calculated. The multivalued estimation in high-light area whose pixel density is low is not correctly calculated and the texture noise is not reduced to desired level.